Abstract
The evident benefits of big data, artificial intelligence and machine learning in society have begun to influence the transition towards a data-driven public sector. Decision-making in the public sector is in an infancy phase of a revolution owing to the inclusion of these new technological innovations. Research has revealed that data-driven e-government policies improve socio-economic development in some nations. Despite the immense opportunities data-driven e-government models have for governments, similar to every system, there are ramifications. This study explores the concept of data-driven e-government as well as investigates the socio-economic implications such an e-government model can have on society. Findings of this exploratory study add insight into a field which is in its early days and still unfocused, as well as making recommendations for policymakers.
Highlights
The data-driven concept has been adopted in a number of fields of endeavor such as education (Marsh et al, 2006), sports and business (Jank, 2011)
The opportunities that come with implementing a data-driven public sector, according to Christodoulou et al (2018), are increased efficiency in decision-making and services provided; public participation and transparency, which strengthens the sense of cooperation between government and citizens; and innovation being birthed in areas such as smart cities
Adopting the line of thought expressed by Hedestig et al (2018), this study extends the work of Agbozo & Spassov (2018) by including the concept of value co-creation, where open government data (OGD) is made readily available to outside, non-typical, stakeholders, with the purpose of developing public value
Summary
The data-driven concept has been adopted in a number of fields of endeavor such as education (Marsh et al, 2006), sports and business (Jank, 2011). In the area of sports, Jank (2011) highlighted successful use-cases such as sports teams (baseball and American football) that are known for using data-analytics in deciding the composition of their teams. This involves the assessment of player performance during training sessions and matches in order to strategize for the game. Examples of successful cases include the prediction of customer/consumer behavior as well as debit and credit card companies employing automated analytic techniques and data-driven strategies in detecting fraudulent activities (Jank, 2011). The aims of research aspirations as far back as 1998 in the area of data-driven marketing are being fully realized and overachieved.
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